Atopic dermatitis (AD) has become a significant public health problem because of increasing prevalence, together with increasing evidence that it may progress to other allergic phenotypes. While it is now acknowledged that AD commonly precedes other allergic diseases, a link termed ‘the atopic march’, debate continues as to whether this represents a causal relationship. An alternative hypothesis is that this association may be related to confounding by familial factors or phenotypes that comanifest, such as early‐life wheeze and sensitization. However, there is increasing evidence from longitudinal studies suggesting that the association between AD and other allergies is independent of confounding by comanifest allergic phenotypes. The hypotheses on plausible biological mechanisms for the atopic march focus on defective skin barrier function and overexpression of inflammatory mediators released by the skin affected by AD (including thymic stromal lymphopoietin). Both human and animal studies have provided evidence supporting these potential biological mechanisms. Evidence from prevention trials is now critical to establishing a causal nature of the atopic march. An emerging area of research is investigation into environmental modifiers of the atopic march. Such information will assist in identifying secondary prevention strategies to arrest the atopic march. Despite much research into the aetiology of allergies, little progress has been made in identifying effective strategies to reduce the burden of allergic conditions. In this context, the atopic march remains a promising area of investigation.

In respiratory health research, interest often lies in estimating the effect of an exposure on a health outcome. If randomization of the exposure of interest is not possible, estimating its effect is typically complicated by confounding bias. This can often be dealt with by controlling for the variables causing the confounding, if measured, in the statistical analysis. Common statistical methods used to achieve this include multivariable regression models adjusting for selected confounding variables or stratification on those variables. Therefore, a key question is which measured variables need to be controlled for in order to remove confounding. An approach to confounder‐selection based on the use of causal diagrams (often called directed acyclic graphs) is discussed. A causal diagram is a visual representation of the causal relationships believed to exist between the variables of interest, including the exposure, outcome and potential confounding variables. After creating a causal diagram for the research question, an intuitive and easy‐to‐use set of rules can be applied, based on a foundation of rigorous mathematics, to decide which measured variables must be controlled for in the statistical analysis in order to remove confounding, to the extent that is possible using the available data. This approach is illustrated by constructing a causal diagram for the research question: ‘Does personal smoking affect the risk of subsequent asthma?’. Using data taken from the Tasmanian Longitudinal Health Study, the statistical analysis suggested by the causal diagram approach was performed.

Rationale: The contribution by asthma to the development of fixed airflow obstruction (AO) and the nature of its effect combined with active smoking and atopy remain unclear. Objectives: To investigate the prevalence and relative influence of lifetime asthma, active smoking, and atopy on fixed AO in middle age. Methods: The population-based Tasmanian Longitudinal Health Study cohort born in 1961 (n = 8,583) and studied with prebronchodilator spirometry in 1968 was retraced (n = 7,312) and resurveyed (n = 5,729 responses) from 2002 to 2005. A sample enriched for asthma and chronic bronchitis underwent a further questionnaire, pre- and post-bronchodilator spirometry (n = 1,389), skin prick testing, lung volumes, and diffusing capacity measurements. Prevalence estimates were reweighted for sampling fractions. Multiple linear and logistic regression were used to assess the relevant associations. Measurements and Main Results: Main effects and interactions between lifetime asthma, active smoking, and atopy as they relate to fixed AO were measured. The prevalence of fixed AO was 6.0% (95% confidence interval [CI], 4.5–7.5%). Its association with early-onset current clinical asthma was equivalent to a 33 pack-year history of smoking (odds ratio, 3.7; 95% CI, 1.5–9.3; P = 0.005), compared with a 24 pack-year history for late-onset current clinical asthma (odds ratio, 2.6; 95% CI, 1.03–6.5; P = 0.042). An interaction (multiplicative effect) was present between asthma and active smoking as it relates to the ratio of post-bronchodilator FEV₁/FVC, but only among those with atopic sensitization. Conclusions: Active smoking and current clinical asthma both contribute substantially to fixed AO in middle age, especially among those with atopy. The interaction between these factors provides another compelling reason for atopic individuals with current asthma who smoke to quit.

Objective To examine asthma remission from childhood to middle age. Methods This was a population-based cohort study. In 1968 the Tasmanian Longitudinal Health Study enrolled 8583 7-year-old Tasmanian schoolchildren who were re-surveyed in 2004. Those reporting ever having asthma when last surveyed completed another questionnaire in 2007 ascertaining age at last asthma attack and asthma medication use. The main outcome measure was asthma remission, defined as no asthma attack for 2 years and no current asthma medication use, or no self-reported asthma in adult life but with parent-reported childhood asthma. Results Of 5729 respondents to the 2004 survey, 1238 self-reported asthma. A further 573 denied asthma, but had parent-reported childhood asthma, giving a study sample of 1811. Asthma had remitted in 1177 (65.0%) of whom 649 (55.1%) were male. Childhood (OR 0.38, 95% CI 0.25 to 0.58) and later-onset allergic rhinitis (0.42, 0.29 to 0.63), childhood (0.66, 0.47 to 0.94) and later-onset eczema (0.66, 0.47 to 0.92), maternal asthma (0.66, 0.47 to 0.92) and childhood chronic bronchitis (0.56, 0.41 to 0.76) were negatively associated with remission. There was weaker evidence for a negative association between passive smoking (0.75, 0.54 to 1.04) and lower socio-economic status (p-trend 0.09) and remission. Childhood-onset asthma (3.76, 2.58 to 5.49) was more likely to remit than adult-onset asthma. Adult smoking was positively associated with remission in childhood-onset asthma (1.49, 1.06 to 2.09). Sex did not influence remission. Conclusion While inherited factors cannot be changed, the effect of allergic rhinitis or eczema on asthma remission might be altered by early, aggressive treatment. Every effort should be made to lessen passive exposure to tobacco smoke.